Matching suitable Feature Construction & Classification for SAR Images based on Particle Swarm Optimization
International Journal of Scientific Research Engineering & Technology (IJSRET)
Synthetic Aperture Radar (SAR) imaging which is used to create image of objects such as landscapes, remote sensing and mappings. The problem of various methods for improvement in SAR image matching features such as noise interference and deviated edges can be improved by using proposed technique. Particle swarm optimization is the nature inspired computational search and optimization approach which was developed on the basis of behavior of swarm. Recently each and every field of research is
... of research is utilizing the properties of PSO. One of the popular fields of research is image segmentation and matching features which is also fastest growing field. Taking the advantages of combining PSO with different image segmentation technique many researchers has proposed various research papers with enhancement of various parameters. In this work, we surveyed and used Swarm Intelligence concept to reduce the unnecessary features from the image to get the desired image with all the highlighted features and try to provide recent trends and techniques involved in improvement in SAR imaging matching features with PSO a computational intelligence technique. The work easily removes the garbage files, which occupies large amount of space, which is responsible for the perfect data cleaning. The PSO methodology can be further enhanced by introducing ACO technique designed for SAR image matching feature for improvement in the images obtained by satellites and more efficiently used in various applications.